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A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly
Journal of Affective Disorders, Volume: 369, Pages: 329 - 337
Swansea University Author: Kyle Jones
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© 2024 The Authors. This is an open access article under the CC BY license.
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DOI (Published version): 10.1016/j.jad.2024.09.147
Abstract
Background: With the continuous advancement of age in China, attention should be paid to the mental well-being of the elderly population. The present study uses a novel machine learning (ML) method on a large representative elderly database in China as a sample to predict the risk factors of depress...
| Published in: | Journal of Affective Disorders |
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| ISSN: | 0165-0327 1573-2517 |
| Published: |
Elsevier BV
2025
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa67859 |
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| Abstract: |
Background: With the continuous advancement of age in China, attention should be paid to the mental well-being of the elderly population. The present study uses a novel machine learning (ML) method on a large representative elderly database in China as a sample to predict the risk factors of depression in the elderly population from both holistic and individual level. Methods: A total of participants met the inclusion criteria from the fourth waves of the China Health and Retirement Longitudinal Study (CHARLS) were analyzed with ML algorithms. The level of depression was assessed by the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). Results: The current study found top 5 factors that were important for predicting depression in the elderly population in China, including average sleep time, gender, age, social activities and nap time during the day. The results also provide reliable diagnostic likelihood at the individual level to support clinicians identify the most impactful factors contributing to patient depression. Our findings also suggested that activities such as interacting with friends and play ma-Jong, chess or join community clubs may have a positive collaborative effect for elderly's mental health. Conclusions: Holistic approaches are an effective method of deriving and interpreting sophisticated models of mental health in elderly populations. More detailed information about a patient's demographics, medical history, sleeping patterns and social/leisure activities can help to inform policy and treatment interventions on a population and individual level. Large scale surveys such as CHARLS are effective methods for testing the most accurate models, however, further research using professional clinical input could further advance the field. |
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| Keywords: |
Machine learning, CHARLS, elderly population, aging, risk factors |
| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
National Social Science Fund of China 22CRK01 |
| Start Page: |
329 |
| End Page: |
337 |